Explain Network Analysis in Terms of Program Evaluation

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The Answer is Analysis of network data is possible at multiple levels. At the highest level, the structure of the network as a whole can be described by metrics like its density (i.e., how many relationships are present), reciprocity (i.e., if A

\rightarrow

B, how often does B

\rightarrow

A), and transitivity (i.e., if A

\rightarrow

B and B

\rightarrow

C, how often does A

\rightarrow

C). Analysis at the network level can be useful for examining broad patterns of change in a system over time, for example, to evaluate whether a program intended to build relationships was effective. At a lower level, it is possible to explore how two actors are positioned with respect to one another in the network. Analysis at the dyad level can be useful for thinking about how actors might affect one another. Finally, at the lowest level, metrics that characterize the position of individual actors can be examined. Here, the focus is often on actors' centrality or the extent to which they occupy an important position in the network. There are many different conceptions of what constitutes an important position, and thus many different measures of centrality, which makes selecting the correct measure particularly important.
Network analysis can also be usefully applied in the field of program evaluation. Process evaluations provide information on how a program works, typically with the goal of understanding "how a program achieves what it does." While process evaluations can be used solely for explanatory purposes (which would not require network analysis), they are also sometimes used for formative purposes to improve programs. However, certain types of process evaluation questions are relational in nature and might benefit from a network analytic approach. For example, evaluators and/or community partners might be interested in asking: Are participants more likely to attend program meetings if their friends also attend these meetings? Answering this question involves collecting relational data on participants' friendships as well as friends' program meeting attendance. Impact evaluation provides information about the extent to which a program works (i.e., has an impact), typically with the goal of making an evaluative judgment about the program. Network analysis is relevant to impact evaluation in cases in which the evaluated program is expected to have an impact on the network or some feature of the network. For example, an evaluator might be interested in asking: Does a new social skills program lead to classrooms with more friendships between students? Here, the outcome of interest--classroom friendships--involves collecting relational data that enumerate who is friends with whom in the classroom.

Explain Network Analysis in Terms of Program Evaluation

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